Times2D: Multi-Period Decomposition and Derivative Mapping for General Time Series Forecasting

2025-03-31

AI Summary

Time-series forecasting faces challenges in modeling complex time-varying dynamics and abrupt fluctuations, where conventional 1D representations struggle to capture multiscale temporal patterns and discontinuities. To address this, we propose a novel time-series 2D-ification paradigm: (i) frequency-domain periodic decomposition disentangles long- and short-term components; (ii) first- and second-order derivative heatmaps (FSDH) explicitly encode local abrupt changes; and (iii) a 2D-CNN backbone coupled with an aggregation forecasting block (AFB) enables end-to-end spatiotemporal modeling. By synergistically integrating spectral analysis with derivative-driven local dynamics modeling, our approach overcomes the representational limitations of 1D models in handling coupled patterns and sharp transients. Extensive experiments on multiple large-scale benchmarks demonstrate state-of-the-art performance for both short- and long-horizon forecasting, significantly outperforming advanced models including Informer and Autoformer.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Time series forecasting is an important application in various domains such as energy management, traffic planning, financial markets, meteorology, and medicine. However, real-time series data often present intricate temporal variability and sharp fluctuations, which pose significant challenges for time series forecasting. Previous models that rely on 1D time series representations usually struggle with complex temporal variations. To address the limitations of 1D time series, this study introduces the Times2D method that transforms the 1D time series into 2D space. Times2D consists of three main parts: first, a Periodic Decomposition Block (PDB) that captures temporal variations within a period and between the same periods by converting the time series into a 2D tensor in the frequency domain. Second, the First and Second Derivative Heatmaps (FSDH) capture sharp changes and turning points, respectively. Finally, an Aggregation Forecasting Block (AFB) integrates the output tensors from PDB and FSDH for accurate forecasting. This 2D transformation enables the utilization of 2D convolutional operations to effectively capture long and short characteristics of the time series. Comprehensive experimental results across large-scale data in the literature demonstrate that the proposed Times2D model achieves state-of-the-art performance in both short-term and long-term forecasting. The code is available in this repository: https://github.com/Tims2D/Times2D.

Problem

• Transforms 1D time series into 2D space for better forecasting
• Captures temporal variations and sharp fluctuations in time series
• Improves accuracy in short-term and long-term time series forecasting

Innovation

• Transforms 1D time series into 2D space
• Uses Periodic Decomposition Block (PDB)
• Incorporates Derivative Heatmaps for sharp changes